Sequential action-induced invariant representation for reinforcement learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Learning task-relevant state representations from high-dimensional, visually distracting observations is a key challenge in visual reinforcement learning.
• Existing unsupervised representation learning methods (bisimulation, contrast, prediction, reconstruction) face limitations in handling distractions and sparse rewards.

Purpose of the Study:

• To develop a robust method for extracting task-relevant state representations in visually distracting environments.
• To improve the performance of reinforcement learning agents by effectively decoupling task-relevant and irrelevant information.

Main Methods:

• Propose Sequential Action-induced invariant Representation (SAR), incorporating action sequences into representation learning.
• Model the characteristic function of action sequence probability distributions to optimize the state encoder.
• Decouple controlled (task-relevant) and uncontrolled (task-irrelevant) information in observations using sequential actions.

Main Results:

• Achieved state-of-the-art performance on the distracting DeepMind Control suite, outperforming strong baselines.
• Demonstrated effectiveness in real-world autonomous driving scenarios (CARLA) with natural distractions.
• Analysis via generalization decay and t-SNE visualization confirmed the method's ability to disregard irrelevant information.

Conclusions:

• SAR effectively extracts task-relevant representations from noisy observations, even with significant visual distractions.
• The method shows strong generalization capabilities and applicability to real-world problems like autonomous driving.
• Leveraging action sequences is a promising direction for improving representation learning in challenging reinforcement learning domains.